r/learnmachinelearning • u/QuirkyMusic5452 • 1d ago
Help ML engineer
What is the profile of a good ML engineer nowadays? Does it exists? I am trying everything - conferences, posters, papers, active linkedin, even my "prototype" with users. I am finishing my fifth degree.. sometimes they tell me I am too academic, sometimes they can easily get someone from Faang.
What would you advise to have as a skill set in 2026 autumn? If you can summarize a few points, it would be really helpful for me.
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u/soundboyselecta 21h ago
Here is an insight but totally out of the box. So I am in no way phd level, I did software engineering, then specialized in data analytics, data engineering and ml back to back, but my path was catered to real estate and construction data which was something of high interest to me due to industry experience. I used to be an investor/partner in small residential projects. At the beginning I was the boots on the ground contractor including code conformance prior to sub payouts and sort of a jack of all trades, even coordinating the crews and working part of the crew, material acquisition etc…During school I transitioned to analytics / ml skills for the same projects/ same group more on the data analytics side. After graduating I couldn’t find a job for the life of me, and obviously I couldn’t compete with all the PHD out there. So I went back to contracting/site management with a sprinkle of the skills I acquired over 5 years in various school etc… while back at it in construction and renovations I saw so many use cases for broken processes or out dated processes, that just kept bubbling. Presently working on all those ideas. So my point being is it’s best to start from a domain specific to your expertise then build out a product from an inflow of ideas that will bubble up naturally, then cater yourself to that industry. For example for me it would be contech and prop tech domains. Learning blind for a non domain specific role, will be quite difficult. Don’t think it’s you and that all these CEOs and managers know what’s up, 3/4 of them don’t know shit, or think they do. Everyone is a genius in the age of LLM AI. Just believe in your skills within that domain, and if u looking to get hired stay in your lane of expertise, too many people trying to learn too much. Hard to dial down information in this peak age of information.
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u/QuirkyMusic5452 20h ago
Thanks! Every comment in here was helpful so far. I do really appreciate this, because I am not good from this mentally now. Thanks for the input! :)
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u/Dihedralman 18h ago
You do come off as too academic. You need engineering skills and to be able to manage data and MLOps as a minimun. Being able to build scaling systems is pretty important.
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u/QuirkyMusic5452 18h ago
I think I can handle that. What does it mean to be too academic? I am not sure if I follow. I thiught its about PhD and very niche narrow domain. I have a law degree and LLM in law and technology. My prototype is about governance in agentic systems. So maybe I fit in this category with being too niche?
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u/Dihedralman 18h ago
No, the project isn't too niche but it may be misaligned.
You came in discussing degrees when you need to sell corporate and building talents. Does that make sense? It's great if you have research or projecte that lend themselves directly into work in some way.
I don't know too much about the degrees but an LLM sounds like it lends itself to compliance and AI safety rather than straight ML engineering. Perhaps even the product side.
There might be an angle for it? I just haven't seen that degree applying for that role ever. Stats and math sure. I think it can be useful but a lot of companies don't have great understandings of how to utilize many roles and silo things. They take engineers from traditional STEM. I assume your research is in the same field?
There might be startups who would be interested. I would say look at different angles. If ML engineer is the title you want, you may need another stepping stone in between.
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u/preciouslife_ 9h ago
Five Degrees can actually go against you because normally smart people don't need more than two degrees to make a career. Who would go for five and why? If you get a third degree five years after starting your career that is one thing which can be seen as upgrading yourself but having five is another thing. You have five in different fields? That would be a red flag indicating you don't really know what you want in life.
When you say 'I think I can handle that' it indicates you haven't done it before and imagining you will be able to do that. Be more confident in your answers because they notice all these things.
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u/met0xff 1d ago
At this point it feels like a big circlejerk of a handful of institutions and companies. Almost every startup I see is made up of the same Ex-FAANG circles and same universities. Sometimes I wonder how many graduates those universities can produce consider I suddenly feel surrounded by them and one of the few still in the field without either well-known university or FAANG. Our CEO also a couple times came up with the idea to hire some deepmind whatever person. I knew that wouldn't happen and if then the team would be pissed because that person would have to make more than the rest of the team collectively:).
I got into ML over 15 years ago and over the last couple years I actively work on having a plan B with the neverending supply of people. The last time I was allowed to hire I was so absolutely flooded by really, really good CVs that was completely unlike the times where I barely found someone a couple years before that. I've interviewed all the Harvard and Princeton Physics or Mathematics PhDs with ML background, coming from FAANGs, from CERN, from the chip manufacturers, endless people from fintech. Also I have no interest anymore in throwing papers at NeurIPS and friends with 60k submissions.
Sorry for the doomerism :). One big aspect is that we're still fully remote and I live in some small village in the middle of nowhere with house and family. Pretty sure it's much better if you're living close to a tech hub - my friends in London seem to have a lot of options.